1  Introduction to Artificial Intelligence

1.1 Successes of Artificial Intelligence

Language

Large Language Model

• ChatGPT (OpenAI): Based on GPT-4, it exhibits human-level performance on various professional and academic benchmarks.
• Claude (Anthropic): Known for large context windows and strong reasoning capabilities.
• Gemini (Google): A multimodal model capable of understanding and reasoning across text, images, audio, video, and code.

Translation

• Real-time Translation: Models like Meta’s SeamlessM4T enable nearly instantaneous speech-to-speech translation across nearly 100 languages.

“The reason Boeing are doing this is to cram more seats in to make their plane more competitive with our products,” said Kevin Keniston, head of passenger comfort at Europe’s Airbus.

\downarrow

Kevin Keniston, người đứng đầu bộ phận thoải mái của hành khách tại Europe’s Airbus, cho biết: “Lý do Boeing làm điều này là để nhồi nhét thêm ghế để máy bay của họ cạnh tranh hơn với các sản phẩm của chúng tôi.’’

Speech Applications

• Siri (Apple): A virtual assistant that can perform tasks such as setting alarms, making calls, and answering questions using natural language.
• Alexa (Amazon): A voice-controlled assistant that can play music, control smart home devices, and provide information.
• Google Assistant: A virtual assistant that can perform tasks such as setting alarms, making calls, and answering questions using natural language.

Google AI Studio

• Google AI Studio: A web-based solution that allows developers to quickly prototype and build AI-powered applications using Google’s AI models.

Auto-captioning

Vinyals et al., 2015

Text-to-Image

OpenAI’s DALL-E

• TEXT PROMPT:

an illustration of a baby daikon radish in a tutu walking a dog

• AI-GENERATED IMAGES:

Vision

• Detection and Segmentation

  

  P. O. Pinheiro, T. Y. Lin, R. Collobert, and P. Dollar. Learning to refine object segments, ECCV, 2016

• Face recognition

  

• Image generation

  Faces: 1024x1024 resolution, CelebA-HQ dataset

  

  T. Karras, T. Aila, S. Laine, and J. Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation, ICLR 2018

• DeepFakes

  

  H. Kim et al., Deep video portraits, SIGGRAPH, 2018

Games

• 2013: DeepMind uses deep reinforcement learning to beat humans at some Atari games
• 2016: DeepMind’s AlphaGo system beats Go grandmaster Lee Sedol 4-1
• 2017: AlphaZero learns to play Go and chess from scratch
• AlphaStar (2019): Achieved Grandmaster level in StarCraft II.
• Cicero (2022): Mastered the game of Diplomacy, combining strategic reasoning with natural language negotiation to cooperate with humans.

Robotics

• Robotic Transformers (RT-2): Google’s vision-language-action (VLA) model that controls robots using web-scale data.
• Humanoid Robots: Tesla Optimus and Boston Dynamics’ Atlas are rapidly evolving to perform complex, dynamic tasks in human environments.

Science

Accelerating Discovery

• AlphaFold 3 (2024): Predicts the structure and interactions of all life’s molecules (proteins, DNA, RNA, ligands) with high accuracy.
• GNoME (2023): Discovered 2.2 million new crystals, accelerating materials science by orders of magnitude.

1.2 What is AI

Intelligence vs. Artificial Intelligence

Definition

• Intelligence includes the capacity for logic, understanding, learning, reasoning, creativity, and problem solving, etc.
• Artificial Intelligence (AI) attempts not just to understand (scientific goal) but also to build (engineering goal) intelligent entities.

The field of Artificial Intelligence

• AI is one of the newest fields in science and engineering.
  • Work started in earnest soon after World War II, and the name itself was coined at a conference at Dartmouth College in 1956.
• AI research aims to build intelligent entities that are capable of simulating humans in different aspects.
  • Thinking: learning, planning, knowledge refinement
  • Perception: see, hear, feel, etc.
  • Communication in natural languages
  • Manipulation and moving objects

What is AI

Thinking Humanly	Thinking rationally
“The exciting new effort to make computers think … machines with minds, in the full and literal sense.” (Haugeland, 1985)	“The study of mental faculties through the use of computational models.” (Charniak and McDermott, 1985)
“The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning …” (Bellman, 1978)	“The study of the computations that make it possible to perceive, reason, and act.” (Winston, 1992)
Acting Humanly	Acting rationally
“The art of creating machines that per- form functions that require intelligence when performed by people.” (Kurzweil, 1990)	“Computational Intelligence is the study of the design of intelligent agents.” (Poole et al., 1998)
“The study of how to make computers do things at which, at the moment, people are better.” (Rich and Knight, 1991)	“AI …is concerned with intelligent behavior in artifacts.” (Nilsson, 1998)

Systems that think like humans	Systems that think rationally
Systems that act like humans	Systems that act rationally

Acting humanly

• The Turing Test approach

• Turing (1950) “Computing machinery and intelligence”

  • “Can machines think?” \longrightarrow “Can machines behave intelligently?”
  • Operational test for intelligent behavior: the Imitation Game. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer

• Problem: Turing test is not reproducible, constructive, or amenable to mathematical analysis

  

Thinking humanly

• The cognitive modeling approach
• Requires scientific theories of internal activities of the brain to get inside the actual workings of human minds
  • What level of abstraction? “Knowledge” or “circuits”?
• How to validate?
  • Predicting and testing behavior of human subjects (top-down) or
  • Direct identification from neurological data (bottom-up)
• These approaches (Cognitive Science and Cognitive Neuroscience) are now distinct from AI
  • Share that the available theories but do not explain anything resembling human intelligence.
  • All share a principal direction.

Thinking rationally

• The “laws of thought” approach
• “Right thinking” is irrefutable reasoning processes
• Based on logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization
• Problems:
  • Not all intelligent behavior is mediated by logical deliberation
  • Solving a problem “in principle” is different from solving it in practice

Acting rationally

• The rational agent approach
• Rational behavior is doing the right thing
  • The right thing which is expected to maximize goal achievement, given the available information
  • Doesn’t necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action
• An agent is an entity that perceives and acts. Abstractly, an agent is a function from percept histories to actions

f:\mathcal{P}^{*} \rightarrow \mathcal{A}

1.3 Foundations of AI

Disciplines to contribute ideas, viewpoints, and techniques to AI

Field	Description
Philosophy	Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality
Mathematics	Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability, probability
Economics	Utility, decision theory, rational economic agents
Neuroscience	Neurons as information processing units
Psychology	How do people behave, perceive, process information, represent knowledge.
Computer Engineering	Building fast computers
Control theory and cybernetics	Design systems that maximize an objective function over time
Linguistic	Knowledge representation, grammar

1.4 History of AI

A brief history of AI

• 1940 – 1950: Early days
  • 1943: McCulloch & Pitts: Boolean circuit model of brain
  • 1950: Turing’s “Computing Machinery and Intelligence”
• 1950 – 1970: Excitement: Look, Ma, no hands
  • 1950s: Early AI programs, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine
  • 1956: Dartmouth meeting: “Artificial Intelligence” adopted
  • 1965: Robinson’s complete algorithm for logical reasoning
• 1970 – 1990: Knowledge-based approaches
  • 1969 – 1980: Early development of knowledge-based systems
  • 1980 – 1988: Expert systems industry booms
  • 1988 – 1993: Expert systems industry busts: “AI Winter”
• 1990 – 2010: Statistical approaches
  • Resurgence of probability, focus on uncertainty
  • General increase in technical depth
  • Agents and learning systems… “AI Spring”?
• 2010 – present: Deep learning and where are we now?

1.5 Course Topics

Solving problems by searching

• Search is the fundamental technique of AI.
  • Possible answers, decisions or courses of action are structured into an abstract space, which we then search.
• Search is either uninformed or informed
  • Uninformed: we move through the space without worrying about what is coming next, but recognizing the answer if we see it
  • Informed: we guess what is ahead, and use that information to decide where to look next.
• We may want to search for the first answer that satisfies our goal, or keep searching until we find the best answer.

Knowledge and reasoning

• The second most important concept in AI
• If we are going to act rationally in our environment, then we must have some way of describing that environment and drawing inferences from that representation.
  • How do we describe what we know about the world?
  • How do we describe it concisely?
  • How do we describe it so that we can get hold of the right piece of knowledge when we need it?
  • How do we generate new pieces of knowledge?
  • How do we deal with uncertain knowledge?

Machine learning

• If a system is going to act truly appropriately, then it must be able to change its actions in the light of experience.
  • How do we generate new facts from old?
  • How do we generate new concepts?
  • How do we learn to distinguish different situations in new environments?

1.6 References